The main aim of this paper is to investigate automatic quality assessment for spoken language translation (SLT). More precisely, we investigate SLT errors that can be due to transcription (ASR) or to translation (MT) modules. This paper investigates automatic detection of SLT errors using a single classifier based on joint ASR and MT features. We evaluate both 2-class (good/bad) and 3-class (go...

This paper reports on an experiment where 795 human participants answered to the questions taken from second language proficiency tests that were translated to their native language. The output of three machine translation systems and two different human translations were used as the test material. We classified the translation errors in the questions according to an error taxonomy and analyzed...

Among the applications of language technology to human translation, little attention has been paid to error detection. This article discusses the issue from three perspectives. In general terms, it considers the nature of translation errors and what might be involved in detecting them. On a more specific level, it identifies a number of error types which meet the twin criteria of feasibility (e...